Successive halving [1]} is a bandit-based multi-fidelity method for efficiently allocating computational resource that gives the most budget to the most promising individuals. Meanwhile, successive halving is an iterative process in which the best half of individuals are retained, and half of the individuals with lower performance are discarded when a portion of the total computational resource runs out. Hyperband [2]} and BOHB [3]} are proposed based on the successive halving, but the former proposed a modification that frequently performs the successive halving method with different budget allocations to find the best solutions. Li et al. [2]} pointed out that because the terminal losses are unknown, the trials of different allocations are helpful to recognise high-quality solutions. For example, two individuals might not have a significant difference on fitness in the 30th epoch, but a greater difference may be observed in the 50th epoch if more computational resources (i.e., epochs) are allocated. Furthermore, BOHB combines Hyperband and BO to chase an ideal state in which performance and computational cost are both covered.
